{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "任务一：\n",
    "输入1（cycle）2（EMG_retus_femoris_l=emg_rf_l），输出4（muscleForce_retus_femoris_l=mf_rf_l）\n",
    "\n",
    "似乎把2做整体输入时，cycle可以省略"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将数据进行分类，每类抽一个作为测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import random\n",
    "import scipy.io as scio\n",
    "import numpy as np\n",
    "from visdom import Visdom\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import torch.nn.init as init\n",
    "from torchsummary import summary\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import datetime\n",
    "#import tool\n",
    "from tool import *\n",
    "import torch.nn.functional as F\n",
    "device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#文件路径\n",
    "#path = 'G:\\科研学习\\肌电信号\\Code\\musle force\\\\'\n",
    "path = '/mnt/data/ZP_Project/Muscle/muscle/'\n",
    "\n",
    "\n",
    "Visdom_Dir = 'Visdom'\n",
    "if not os.path.exists(path+Visdom_Dir):\n",
    "    os.makedirs(path+Visdom_Dir)\n",
    "image_folder ='image_Mission1_FC_all_seperate'\n",
    "\n",
    "\n",
    "if not os.path.exists(path + image_folder):\n",
    "        os.makedirs(path + image_folder)\n",
    "\n",
    "\n",
    "filenames = os.listdir(path+'Transed_Data')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
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      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n"
     ]
    }
   ],
   "source": [
    "# 可视化\n",
    "timeForSave = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n",
    "\n",
    "class visdom_account:\n",
    "    def __init__(self):\n",
    "        self.port = 8097\n",
    "        self.server = \"http://localhost\"\n",
    "        self.base_url = \"/\"\n",
    "        self.username = \"admin\"\n",
    "        self.passward = \"1234\"\n",
    "        self.evns = \"Muscle\"\n",
    "\n",
    "\n",
    "viz_acnt = visdom_account()\n",
    "\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz.delete_env('Muscle')  # 删除环境\n",
    "\n",
    "# viz.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#          opts={'title': 'ProcessMonitor', },)\n",
    "# viz1.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#           opts={'title': 'ProcessMonitor', },)\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz1 = Visdom(env=viz_acnt.evns,\n",
    "              log_to_filename=Visdom_Dir+'vislog_'+timeForSave)\n",
    "viz_batch = []\n",
    "for i in range(32):\n",
    "    viz_batch.append(Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the searching word is :Fast\n",
      "Transformed_redacted_GIL12_Fast2.mat\n",
      "Transformed_redacted_GIL04_Fast2.mat\n",
      "Transformed_redacted_GIL02_Fast3.mat\n",
      "Transformed_redacted_GIL11_Fast2.mat\n",
      "Transformed_redacted_GIL08_Fast1.mat\n",
      "Transformed_redacted_GIL06_Fast3.mat\n",
      "Transformed_redacted_GIL03_xFast2.mat\n",
      "Transformed_redacted_GIL01_Fast5.mat\n",
      "the searching word is :Free\n",
      "Transformed_redacted_GIL02_Free2.mat\n",
      "Transformed_redacted_GIL04_Free3.mat\n",
      "Transformed_redacted_GIL03_Free4.mat\n",
      "Transformed_redacted_GIL11_Free1.mat\n",
      "Transformed_redacted_GIL12_Free4.mat\n",
      "Transformed_redacted_GIL06_Free2.mat\n",
      "Transformed_redacted_GIL01_Free4.mat\n",
      "Transformed_redacted_GIL08_Free1.mat\n",
      "the searching word is :XSlow\n",
      "Transformed_redacted_GIL03_XSlow1.mat\n",
      "Transformed_redacted_GIL11_XSlow1.mat\n",
      "Transformed_redacted_GIL08_XSlow2.mat\n",
      "Transformed_redacted_GIL06_XSlow1.mat\n",
      "Transformed_redacted_GIL01_XSlow2.mat\n",
      "Transformed_redacted_GIL04_XSlow1.mat\n",
      "Transformed_redacted_GIL02_XSlow2.mat\n",
      "Transformed_redacted_GIL12_XSlow1.mat\n",
      "the searching word is :slow\n",
      "Transformed_redacted_GIL12_slow4.mat\n",
      "Transformed_redacted_GIL04_slow3.mat\n",
      "Transformed_redacted_GIL06_slow2.mat\n",
      "Transformed_redacted_GIL08_slow4.mat\n",
      "Transformed_redacted_GIL03_slow3.mat\n",
      "Transformed_redacted_GIL11_slow2.mat\n",
      "Transformed_redacted_GIL01_slow3.mat\n",
      "Transformed_redacted_GIL02_slow1.mat\n"
     ]
    }
   ],
   "source": [
    "#读取并合并\n",
    "#尝试直接把数据相加\n",
    "\n",
    "\n",
    "set = []\n",
    "key_words =['Fast','Free','XSlow','slow']\n",
    "for word in key_words:\n",
    "    data_emg = []\n",
    "    data_mf = []\n",
    "    print('the searching word is :'+str(word))\n",
    "    for i in range(len(filenames)):\n",
    "        filename = filenames[i]\n",
    "        if word in filename:\n",
    "            print(filename)\n",
    "            data = scio.loadmat(path+'Transed_Data/' + str(filename))\n",
    "            filenames[i] = 'None'\n",
    "            data1 = data['emg_rf_l']\n",
    "            #data1 = data1.squeeze()\n",
    "            data2 = data['mf_rf_l']\n",
    "            #data2 = data2.squeeze()\n",
    "            data_emg.append(data1)\n",
    "            data_mf.append(data2)\n",
    "\n",
    "    data_emg = np.array(data_emg)\n",
    "    data_mf = np.array(data_mf)\n",
    "    set.append(MuscleData1(data_emg,data_mf))\n",
    "\n",
    "    # sampel = next(iter(set))\n",
    "    # print(sampel,'\\nnnnnnn')\n",
    "\n",
    "# train_dataset, test_dataset = torch.utils.data.random_split(set,[28,4])\n",
    "\n",
    "# train_loader =torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True, pin_memory=False)\n",
    "# test_loader =torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, pin_memory=False)\n",
    "# train_test_loader = torch.utils.data.DataLoader(set, batch_size=1, shuffle=True, pin_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"此代码块为Net训练\"\"\"\n",
    "Net = []\n",
    "train_loader =[]\n",
    "test_loader = []\n",
    "for i in range(4):\n",
    "        net = FC_Net()\n",
    "        net.apply(weights_init)\n",
    "        net.to(device)\n",
    "        Net.append(net)\n",
    "        train_set,test_set = torch.utils.data.random_split(set[i],[len(set[i])-1,1])\n",
    "        train_loader.append(torch.utils.data.DataLoader(train_set, batch_size=4, shuffle=True, pin_memory=False))\n",
    "        test_loader.append(torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=False))\n",
    "\n",
    "\n",
    "criterion = torch.nn.MSELoss()\n",
    "epoch_num = 10000\n",
    "for i in range(4):\n",
    "        optimizer = torch.optim.SGD(Net[i].parameters(),\n",
    "        lr=0.0000015,momentum=0.9)\n",
    "        vizx = 0\n",
    "        #开始训练\n",
    "        for epoch in range(epoch_num):\n",
    "                #n = 0   #此n用于visdom画图。每个epoch对每个样本进行画图\n",
    "                for batch in train_loader[i]:\n",
    "                        vizx+=1\n",
    "                        data,label = batch\n",
    "                        data = torch.as_tensor(data).to(device)\n",
    "                        #print(data.shape)       #torch.Size([1, 101, 1])\n",
    "                        label =torch.as_tensor(label).to(device)\n",
    "                        label = label.squeeze(2)\n",
    "                        #print(data.shape)\n",
    "                        #data = data.reshape(-1,1,1)\n",
    "                        #print('data',data.shape)\n",
    "                        #data = data.reshape(-1,1,101)  #CNN需要,FC时注释掉\n",
    "                        pred = Net[i](data)\n",
    "                        #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "                        #print('pred',pred.shape)\n",
    "                        #print('label',label.shape)     #label torch.Size([101, 1])\n",
    "                        loss = criterion(pred,label)\n",
    "                        optimizer.zero_grad()\n",
    "                        loss.backward()\n",
    "                        #viz.line([float(loss)],[vizx],win='loss', name='loss-batch', update='append')\n",
    "                        viz_batch[i].line([float(loss)],[vizx],win='loss'+str(i), name='loss'+str(i), update='append',opts=dict(title='loss'+str(i)))\n",
    "                        optimizer.step()\n",
    "                        #n +=1\n",
    "                        #print(loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0th class\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1th class\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2th class\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3th class\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "i =0\n",
    "for m in range(len(test_loader)):\n",
    "        print('{}th class'.format(m))\n",
    "        for batch in test_loader[m]:\n",
    "                data,label = batch\n",
    "                data1 = data.to(device) #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "                # label =label.to(device)\n",
    "                #data1 = data1.reshape(-1,1,101)  #CNN需要,FC时注释掉\n",
    "                pred = Net[m](data1)\n",
    "                #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "                pred = pred.cpu()\n",
    "                pred = pred.data.numpy()\n",
    "                pred=np.squeeze(pred)\n",
    "                label=np.squeeze(label)\n",
    "                data = np.squeeze(data)\n",
    "                #利用plot画图\n",
    "                plt.figure(i)\n",
    "                plt.title('class'+str(m)+'sample'+str(i))\n",
    "                plt.plot(pred, label='pred')\n",
    "                plt.plot(label, color='red', label='GroundTruth')\n",
    "                plt.plot(data*100, color='black', label='data')\n",
    "                plt.legend()\n",
    "                figname ='./image_Mission1_FC_all_seperate/imag_ {}.jpg'.format(i)\n",
    "                plt.savefig(figname)\n",
    "                plt.show()\n",
    "                plt.close()\n",
    "                i +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Net[0].state_dict().keys()\n",
    "# for i in range(len(test_loader)):\n",
    "#     print(i)\n",
    "#     # print(Net[i].fc1.weight)\n",
    "#     # print(Net[i].fc2.weight)\n",
    "#     # print(Net[i].out.weight)\n",
    "#     w1 =Net[i].fc1.weight.cpu().data.numpy()\n",
    "#     w2 =Net[i].fc2.weight.cpu().data.numpy()\n",
    "#     w3 =Net[i].out.weight.cpu().data.numpy()\n",
    "#     w1 = np.squeeze(w1)\n",
    "#     w2 = np.squeeze(w1)\n",
    "#     w3 = np.squeeze(w1)\n",
    "#     #ss\n",
    "#     plt.figure(i)\n",
    "#     plt.title('Net'+str(i))\n",
    "#     plt.plot(w1, label='fc1.weight')\n",
    "#     plt.plot(w2, color='red', label='fc2.weight')\n",
    "#     plt.plot(w3, color='black', label='out.weight')\n",
    "#     plt.legend()\n",
    "#     #figname ='./image_Mission1_FC/imag_ {}.jpg'.format(i)\n",
    "#     #plt.savefig(figname)\n",
    "#     plt.show()\n",
    "#     plt.close()\n",
    "    "
   ]
  }
 ],
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